Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially with the number of robots and task complexity, making them challenging for online solution. To accelerate MR-TP over a system's lifetime, this work looks at combining two recent advances: (i) Decomposable State Space Hypergraph (DaSH), a novel hypergraph-based framework to efficiently model and solve MR-TP problems; and \mbox{(ii) learning-by-abstraction,} a technique that enables automatic extraction of generalizable planning strategies from individual planning experiences for later reuse. Specifically, we wish to extend this strategy-learning technique, originally designed for single-robot planning, to benefit multi-robot planning using hypergraph-based MR-TP.
@article{arxiv.2409.10692,
title = {Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs},
author = {Khen Elimelech and James Motes and Marco Morales and Nancy M. Amato and Moshe Y. Vardi and Lydia E. Kavraki},
journal= {arXiv preprint arXiv:2409.10692},
year = {2024}
}